Instantaneous R for COVID-19 in Turkey: Estimation by Bayesian Statistical Inference

Şenel, Kerem; Özdinç, Mesut; Öztürkcan, Selcen; Akgül, Ahmet

doi:10.5336/medsci.2020-76462

Cited by 8 publications

(6 citation statements)

References 2 publications

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“…Estimating the instantaneous reproduction number by using a rather robust method such as Bayesian statistical inference can shed light on the optimal timing for easing limitations. 44,45 About the Authors…”

Section: Discussionmentioning

confidence: 99%

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

Şenel

Özdinç

Öztürkcan

2020

Disaster med. public health prep.

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The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic. K-means algorithm is used to perform a cluster analysis of the top ten countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness. As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited. We propose a SPE (“Single Parameter Estimation”) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy and decision-makers in shedding light on the next phases of the pandemic.

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Section: Discussionmentioning

confidence: 99%

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

Şenel

Özdinç

Öztürkcan

2020

Disaster med. public health prep.

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“…where w(t) is the probability distribution of the generation time of the outbreak, which can be considered as the probability distribution of the interval between successive cases of the illness [38]; and R(t) is the instantaneous reproduction number represented as…”

Section: Estimation Of the Instantaneous Reproduction Numbermentioning

confidence: 99%

An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries

Leiva,

Alcudia,

Montano

et al. 2023

Biology

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This research provides a detailed analysis of the COVID-19 spread across 14 Latin American countries. Using time-series analysis and epidemic models, we identify diverse outbreak patterns, which seem not to be influenced by geographical location or country size, suggesting the influence of other determining factors. Our study uncovers significant discrepancies between the number recorded COVID-19 cases and the real epidemiological situation, emphasizing the crucial need for accurate data handling and continuous surveillance in managing epidemics. The absence of a clear correlation between the country size and the confirmed cases, as well as with the fatalities, further underscores the multifaceted influences on COVID-19 impact beyond population size. Despite the decreased real-time reproduction number indicating quarantine effectiveness in most countries, we note a resurgence in infection rates upon resumption of daily activities. These insights spotlight the challenge of balancing public health measures with economic and social activities. Our core findings provide novel insights, applicable to guiding epidemic control strategies and informing decision-making processes in combatting the pandemic.

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“…Consequently, we first describe the mathematical model that we have used; then, we compute the number of trips that are produced and attracted in each borough of Mexico City using data about these trips in 2017 [24], which then we combine with the rates of reduction or increase in mobility during the pandemic reported by Google [25] and the government of Mexico City [26]. Later, by using Bayesian inference, we solve the associated inverse problem to predict the dynamics of the spread of cases, similar to the following references [27][28][29][30][31][32][33]. Our conclusions are presented in the last section.…”

Section: Introductionmentioning

confidence: 99%

On mobility trends analysis of COVID–19 dissemination in Mexico City

2022

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This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.

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Instantaneous R for COVID-19 in Turkey: Estimation by Bayesian Statistical Inference

Cited by 8 publications

References 2 publications

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

An Epidemiological Analysis for Assessing and Evaluating COVID-19 Based on Data Analytics in Latin American Countries

On mobility trends analysis of COVID–19 dissemination in Mexico City

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